You've probably heard the term machine learning (ML) used a lot lately in consumer tech circles and among software developers. However, you may not know what it exactly consists of, how it works, and whether it's worth investing your time and energy into learning.
Machine learning and deep learning (DL) are subsets of artificial intelligence (AI). These three terms are often used interchangeably despite the fact that they relate to different concepts within the larger picture of AI. This guide will look at each of these terms, explore the uses of machine learning today, and give you some tips for getting started with machine learning development.
Let's begin by defining some terms related to machine learning.
The general definition of AI is a computer system or any form of hardware or machine that takes on human functions with specialized algorithms. It could be simply a web app or software running on a phone, computer, or robot. Or it could be a whole network of servers working alongside each other running an AI algorithm for something like banking software.
General Artificial Intelligence is an idea that involves not just algorithms focusing on specific artificial intelligence tasks, but a computational system that has a similar level of learning capability and intelligence as a human brain. Basically, it would have a similar level of understanding and possibly consciousness as an end result—if that is even possible, which we cannot fully know yet.
AI algorithms allow computing systems to accomplish tasks that have traditionally been performed only by humans. Examples of such tasks include calculating the probabilities of a car being able to parallel park safely; automating factory tasks humans had to previously do one at a time; or translating complex text automatically.
These tasks are possible as computing systems learn behaviors after being operational for a period of time, self-improve by analyzing data, and predict future results (predictive analysis) from it. AI software is designed to aid end users and companies by giving them information on a scale they would not be able to access with human brainpower alone. There is just too much data to process, including both structured and unstructured data.
ML specifically deals with the ability of computer algorithms to learn over time through usage. These systems tend to improve functionality and effectiveness the longer they stay operational because they find more efficient solutions over time. This comes as the algorithms deal with adverse conditions or a wide variety of interactions with end users.
DL is actually a subset of machine learning and thus also a subset of AI. It mimics aspects of the human brain with its ability to process large amounts of data and makes sense of this data on an intelligent scale. This process is also called business intelligence or predictive analytics.
DL does this in far larger quantities than humans could ever dream of. It uses algorithms to find patterns, issues, and insights from data, including unstructured data that is hard to define or analyze by traditional means. Examples of such data are social media posts or videos uploaded to YouTube.
Instead of just being taught to process and learn from raw data, computers teach themselves over time using a system similar to the human brain, in which neurons transfer information on paths known as neural circuits with junctions called synapses that connect different parts of the brain. These parts of the brain are arranged hierarchically as layers, and information is processed based on these layers. For instance, you may smell food first before realizing what it is and that you love the way it tastes. The concept of deep learning is loosely based on a similar arrangement that relies on algorithms that intercept data within a larger network to find patterns for future insight.
In deep learning, data is processed and analyzed through a layering process. Various layers are used to extract information at progressively higher levels from raw inputs of data. This is done through an artificial neural network (ANN) consisting of an input layer (where data is entered), a hidden layer (where information is processed), and an output layer (where decisions are made with data in mind). Information gained from one layer is already processed and available when dealing with new information.
Both machine learning and its deep learning subset have a bright future and will be increasingly adopted by companies of all sizes that want to get a step up on competition and read their audiences better. These concepts are not just catchphrases anymore, but technologies that affect all aspects of tech development and make life easier for anyone who uses them to gain new insight.
ML has uses in a wide range of sectors and industries. Chatbot technologies are constantly improving their use of ML algorithms to enable businesses to streamline their communications with clients. A chatbot is a messenger bot that allows clients to ask questions and get answers related to products while interacting with the company even during off hours. Over time, ML allows the chatbot to learn the types of questions customers ask, how to interpret various ways of asking a question, and how to answer in the best way possible.
Voice recognition is another example of a technology that uses machine learning. There are many voice voice assistant systems that rely on AI systems to help users accomplish a variety of tasks. The voice assistants include Amazon’s Alexa, tied to Amazon Echo and other Amazon hardware, as well as Apple‘s Siri, which is tied to HomePod, iOS, and Mac hardware.
Voice recognition technologies are truly an example of software paving the way for hardware to change and adapt to user needs. Voice assistant systems powered by ML algorithms can be used to assist customers with daily tasks, such as checking the weather or sports, as well as to automate things like finding a playlist of their favorite music tracks or turning the light on at specific times of the day in a smart home.
You may also have heard of driverless cars and their potential to replace human drivers. Google is particularly notable with its fleet of driverless vehicles currently driving around autonomously with cameras on their hoods mapping terrain for the Google Maps application. In the meantime, driverless cars are in a testing phase in order to become commercially viable. Machine learning algorithms are allowing them to improve the longer they stay on the roads and deal with the unpredictability of human error, weather, and parking conditions. The difference between a potential parking spot and a private driveway or a fall-off cliff can be hard to spot without experience and learning through ML.
The medical field is benefitting from ML and DL advancements as a whole. For example, in radiology image analysis, ML and DL algorithms are able to find patterns in scans of fractures and other injuries or, in some cases, detect injuries before the human eye can. The idea, however, as in other industries, is not for AI to replace clinicians, but to take on part of their workload to help them be more efficient and to give patients some control in terms of self care.
These are just a few examples where machine learning is making an impact along with AI as a whole. The potential is almost endless as really any industry can benefit, from banking systems becoming more automated and less prone to errors or human fraud to the Internet of Things (IoT) impacting Smart Homes and manufacturing.
The results of ML development are easier to see in some technologies, like the aforementioned self-driving cars, than others. When looking at modern robots and comparing them to older models, it's easy to see improvements in the way they function and have learned to interact with humans over time.
However, more mundane tasks, like seeing patterns in data and finding insight from these patterns, are also a part of ML. Finding needles in haystacks can prevent things like cybersecurity threats. An example would be a backdoor vulnerability exploited periodically by a certain source (a geographical region, for instance, or even software exploitation that is enabled in the app) that ML could catch and prevent.
Cybersecurity is really a great example of where ML can have a huge impact due to how algorithms access huge amounts of data and are able to process this data in real time. For instance, when someone uses a credit card to make purchases from a country they do not reside in, an algorithm can pick this up quickly and alert both the client and the credit card company.
Humans often make assumptions without actually corroborating and fact checking these assumptions. Data science, for instance, is often plagued with bias. ML can corroborate or disprove assumptions and theories as it has access to a wide range of data it can analyze in real time. ML can be useful in both gaining current insight and predicting future trends for business and even worldwide issues like global warming.
With so many industries affected and so much media attention on machine learning, you might be asking: “Is it all hype? Is this just a fad or is it really here to stay for the long term?" The answer is the latter, and here's why.
We are still in the early phases of what machine learning is capable of. The technology is just beginning to penetrate many industries like banking, the medical field, and construction. It's changing robotics through Robotic Process Automation (RPA) and affecting medicine through things like X-ray scan analysis. The amount of interest and investment in machine learning is showing just how hot this technology is and how eager business are to embrace it before the competition does and leaves them hanging.
You can look at the example of Amazon as a company that is truly striking it big and benefitting from being an early adopter of ML in consumer tech and other aspects of its operations. According to Forbes, ML is so important to Amazon, the company claims that “without ML, Amazon.com couldn’t grow its business, improve its customer experience and selection, and optimize its logistic speed and quality.”
Amazon is just one example. Many startups and smaller companies are also finding unique ML uses to leverage for their audiences. Mobile and web apps exist, for example, that allow automated translations of text from one language to another. This is akin to what Google Translate is doing, but with more specialization. An example is DeepL. Unlike Google, which mostly focuses on automation of standard speech patterns and basic questions a user might ask when traveling abroad, DeepL focuses on nuances within speech and linguistic variants in order to allow better translations of large walls of text or entire articles and documents.
There are also some interesting mobile apps for physicians and healthcare professionals that use ML. BiliScreen uses a mobile device's camera to look into patients' eyes and find symptoms of disease such as that of pancreatic cancer. It's still in the testing phases and may soon become widely available.
The potential of ML is endless and is already drawing businesses worldwide to invest hugely in machine learning training and software developers familiar with machine learning algorithms. This is true for both small startups and large enterprises.
If you are a software developer, proficiency in machine learning principles and the languages used to implement ML algorithms into products and services will make you much more competitive and desirable. Now is a great time to get on the ML bandwagon as the tech is still nascent and will continue to grow and become more widespread, thus potentially becoming a requirement for developers to grasp.
A great way to get started with AI and ML development is to learn the Python language. Python is widely used for ML development because of the way it handles libraries, which take a lot of the heavy lifting away from coders.
Another reason Python is often used by software developers for machine learning is that web-based machine learning models are a popular way to develop with ML principles in mind. Python is not used to construct the models per se, but it is used as a wrapper for libraries associated with the models that do the trick.
The libraries directly associated with ML are often difficult for the average developer to work with or, in some cases, to grasp. Python takes this headache away by delivering a high-level instruction set to libraries to help them handle ML workflows effectively. Python removes many of the nuanced behaviors a developer would have to deal with when working with these original languages and their libraries, such as memory management and handling of objects. This is why it's a good entry point for ML development.
Other good examples of programming languages useful for ML development are R and Julia.
Like Python, R also has a wide range of libraries and tools for ML development as well as packages that offer the great performance ML often needs. The biggest difference between R and Python is that while Python was developed as a general purpose language, R was developed with more specific uses in mind. It is tailored to statistical analysis rather than a wide range of programming tasks or goals. Both are also open source languages.
Julia may be the next big thing in this space worth watching as the language to learn. Its strengths include great performance for application deployment and top support for modern frameworks such as TensorFlow. Thus, it is easy to integrate into existing workflows.
TensorFlow is an open-source library from Google specifically designed for ML development. It's worth learning and delving into for various reasons. First, it's compatible with Juliet, R, and Python. It's useful for deep learning models, and its Keras API provides a high-level interface for neural networks.
Another tool worth exploring is Databricks. It offers a machine learning-focused workload called Databricks Runtime ML, which is a ready-to-go-environment for both ML and data science as a whole.
Before embarking on your ML journey, however, make sure it's something that interests you and you want to be part of for the long term. Pluralsight has a wide range of introductory courses that you should check out in order to both understand the concepts behind ML and get a feel for whether this sector of development is for you.
In addition to learning development languages, you should also gain some knowledge of statistics and mathematics. Take a statistics course to familiarize yourself with concepts such as correlation. A good foundation in math, statistics, and programming can benefit you in machine learning development, sometimes referred to as 'data-driven AI.'
Machine learning is not going away anytime soon. The field is in its infancy, just starting to be used across a wide range of industries to improve products and services. Along with its deep learning subfield, it is finding answers to questions no one could predictably answer before by handling large data sets quickly and making sense of them through predictive analysis and business intelligence.
Don't wait and have to play catch up later in this space. Start absorbing ML principles today.